Name: 1x ThePi7on Solidd Deborutify UltraLite
Author: ThePi7on

This model corrects jagged lines on animation that has been deinterlaced. It handles simple line doubling, line interpolation, and even Yadif-style artifacts. It can also handle sources that were resized after deinterlacing, for example resizing from ntsc to pal resolutions. If a source has been upscaled after deinterlacing, it will need to be downsized before applying this model.

AnimeUndeint Compact

Pretrained: 4xPSNR

Texture retaining denoiser and sharpener for digital artwork

Sample images: https://1drv.ms/u/s!Aip-EMByJHY28xKFp93VIGydNsoN?e=vN2WYh

ArtClarity

Category: Luminance/Chroma

ChromaSharpen - makes the colors slightly more vibrant with a sideeffect of possibly adding Chromatic Abberation to the image. I am not sure about the usage of this model on real case scenarios but anything blurry or with fuzzy colors could work

BSChroma

This model helps repair color bleed and heavy chroma noise that may be present on some older footage, particularly that which was recorded on VHS. It also cleans up rainbows if they are present.

BleedOut Compact

Purpose: Cartoons

This is a simple retrain of SaurusX's 1x_BroadcastToStudioLite_485k model from a couple years ago. This one is trained on compact, actually has less artifacts, and is significantly faster.

__Comparisons:__ <https://slow.pics/c/oGwHyYym>
![1733071729 5752351](https://github.com/user-attachments/assets/f247b870-d49e-4c37-ad39-c4565efe164d)

BroadcastToStudio Compact

DeBLR

This is a 1x model for removing halos in animation. I was training this a while back and considered it a failed project, but I recently found it useful on a few things I was working on, so I decided to release it. I don't remember any details regarding how it was trained.

Dehalo_v1_Compact

Wipes out dot crawl and rainbows in animation.

Dotzilla Compact

Pretrained model: 1x_DeJpeg_Fatality_01_175000_G.pth

Fatality DeBlur

These models deblur most images. It was trained mostly on aniso2 and iso blurring (BSRGAN augmentation) with some gaussian mixed in. It performs well on most blurry images, but I'd recommend using something like Fatality_Deblur for very strong gaussian blur.

Focus

Focus Moderate

This is a sharpening/deblurring model for anime video. It was created with three goals in mind:
Be blazing fast
Try to avoid enhancing noise and compression artifacts
Try to avoid over-enhancing intentionally blurred parts of the image

Despite that last point, this is not intended to be used on modern anime which makes heavy of use depth-of-field effects.

HurrDeblur SuperUltraCompact

Category: Skin
Purpose: Skin Upscaling

Adds plausible high frequency detail and removes subtle blur. This is an early unfinished attempt at a x1 Lite model designed specifically for enhancing detail on skin diffuse textures of 3d characters. Even in its current state it works quite well.

Best suited for uncompressed or cleaned textures - otherwise it may just enhance any existing compression artefacts too. The training set included faces, body parts, eyes and hair in a variety of skin types and tones so it should work well on most related diffuse textures.

However it's not just limited to skin, many other images and textures can be enhanced with this model. The results are subtle, so run multiple times if desired.

Pretrained model: 50/50 Interpolation of DIV2K-Lite and SpongeBC1-Lite

ITF SkinDiffDetail Lite v1

PixelSharpen

DeBlur, ReFocus, Sharpen Manga, Anime and cartoon style images, but will work on real life images too.

ReFocus Cleanly

DeBlur, ReFocus, Sharpen Real life style images, but will work on Anime images too.

ReFocus V3

Takes composite/RF/RGB/VHS Genesis footage and attempts to restore it to RGB quality. Assumes footage has been properly deinterlaced via field duplication from 240p to 480p/720p/etc. Note that:

* All footage was captured in 240p/480p/720p NTSC.
* Ground truth RGB footage was captured via a triple-bypassed Model 2 Genesis with new capacitors via the Retrotink 2x or GBS Control. Other footage was captured from various Model 1 Genesis consoles (with and without the "High Definition Graphics" mark).
* The model was trained exclusively on individual frames, so it can't fix things like dropouts.
* Should help fix color, blur, and jailbars.
* Cannot un-dither fully blended pixels.

Revision History:

1.0.0 (09/02/2025): Initial release.

SEGA Genesis Cleanup

Purpose: For restoring the non blu-ray Evangelion episodes.

Restoration model for de-cruddifying Evangelion episodes from their non blu-ray source. Has issues with eyes in some cases, where it thinks the whites are haloing. Comparison images are of the interpolated model.

SPANGELION

SPANGELION INTERPOLATED

SPANGELION SHARP

Purpose: Animation - Quantized

Removed color quantization/indexing and dithering from cartoon style images and textures

Pretrained model: 1x_1xDEDITHER_32_512_126900_G

Spongebob De-Quantize

Sharpening, line darkening and slight line thinning, specifically made for the Boruto anime.

ThePi7on Solidd Deborutify UltraLite

Fix images that have been arbitrarily / poorly resized, such as non-integer nearest-neighbor upscaling/downscaling - Also acts as an image sharpener/deblur when used on slightly soft inputs

Pretrained model: 1x_UnResize_MKII_030000_G.pth

UnResize V3

mdeblur

In addition to removing the vertical blur, the model upscales, sharpens and will remove MPEG-2 artifacting and a small amount of rainbowing and dot crawl. Another series afflicted with the vertical blur is Avatar the Last Airbender, which can be repaired by this model. The video fed into the model MUST be 540 vertical for the deblur to work properly.

Architecture	ESRGAN
Scale	1x
Size	10nf5nb
Color Mode	RGB
License	CC-BY-NC-SA-4.0 Private use Distribution Modifications Credit required Same License State Changes No Liability & Warranty Disclaimer
Date	2021-05-09
Dataset	Some frames from the sharper shippuden episodes
Training iterations	260000
Training epochs	2363
Training batch size	1
Training HR size	128
Training OTF	No

ThePi7on Solidd Deborutify UltraLite

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